| Title | A Generative Adversarial Approach for Sybil Attacks Recognition for Vehicular Crowdsensing |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Jaimes, Luis G., Calderon, Juan, Shriver, Scott, Hendricks, Antonio, Lozada, Javier, Seenith, Sivasundaram, Chintakunta, Harish |
| Conference Name | 2022 International Conference on Connected Vehicle and Expo (ICCVE) |
| Date Published | mar |
| Keywords | composability, connected vehicles, Crowdsensing, Data collection, Market research, Metrics, Neural networks, pubcrawl, Resiliency, sybil attacks, Training, Trajectory |
| Abstract | Vehicular crowdsensing (VCS) is a subset of crowd-sensing where data collection is outsourced to group vehicles. Here, an entity interested in collecting data from a set of Places of Sensing Interest (PsI), advertises a set of sensing tasks, and the associated rewards. Vehicles attracted by the offered rewards deviate from their ongoing trajectories to visit and collect from one or more PsI. In this win-to-win scenario, vehicles reach their final destination with the extra reward, and the entity obtains the desired samples. Unfortunately, the efficiency of VCS can be undermined by the Sybil attack, in which an attacker can benefit from the injection of false vehicle identities. In this paper, we present a case study and analyze the effects of such an attack. We also propose a defense mechanism based on generative adversarial neural networks (GANs). We discuss GANs' advantages, and drawbacks in the context of VCS, and new trends in GANs' training that make them suitable for VCS. |
| DOI | 10.1109/ICCVE52871.2022.9743106 |
| Citation Key | jaimes_generative_2022 |
A Generative Adversarial Approach for Sybil Attacks Recognition for Vehicular Crowdsensing